We sought to develop a multiple biomarker approach for prediction of incident major adverse cardiac events (MACE; composite of cardiovascular death, myocardial infarction, and stroke) in patients referred for coronary angiography. In a 649-participant training cohort, predictors of MACE within 1 year were identified using least-angle regression; over 50 clinical variables and 109 biomarkers were analyzed. Predictive models were generated using least absolute shrinkage and selection operator with logistic regression. A score derived from the final model was developed and evaluated with a 278-patient validation set during a median of 3.6 years follow-up. The scoring system consisted of N-terminal pro B-type natriuretic peptide (NT-proBNP), kidney injury molecule-1, osteopontin, and tissue inhibitor of metalloproteinase-1; no clinical variables were retained in the predictive model. In the validation cohort, each biomarker improved model discrimination or calibration for MACE; the final model had an area under the curve (AUC) of 0.79 (p <0.001), higher than AUC for clinical variables alone (0.75). In net reclassification improvement analyses, addition of other markers to NT-proBNP resulted in significant improvement (net reclassification improvement 0.45; p = 0.008). At the optimal score cutoff, we found 64% sensitivity, 76% specificity, 28% positive predictive value, and 93% negative predictive value for 1-year MACE. Time-to-first MACE was shorter in those with an elevated score (p <0.001); such risk extended to at least to 4 years. In conclusion, in a cohort of patients who underwent coronary angiography, we describe a novel multiple biomarker score for incident MACE within 1 year (NCT00842868).